Infrared and visible image fusion (IVIF), referring to providing the typical characteristics from source images, has been witnessed rapid development in recent years. IVIF can effectively break the limitation of information loss from single sensor and plays an important role
for the follow-up high-level vision tasks, e.g., object detection [1]}, semantic segmentation [2]} and so on. Unfortunately, there are few works [3]}, [4]}, [5]} to bridge high-level semantic tasks with image fusion. In this paper, rather than explicitly construct fusion rules for IVIF, we propose a generic semantic-driven learning paradigm to investigating task-specific image fusion.
